Data characteristics of this category
Data for this category comes from elderly care service information platforms, civil affairs statistics systems, fire safety inspection records, and institutional operation ledgers. Updates occur monthly. Commonly processed documents include institutional filing materials, operation subsidy applications, service quality inspection forms, safety hazard rectification ledgers, and elderly admission files. Most fields cover institutional entity information, bed count, service visits, rectification time limits, and subsidy application details. Supported units include beds, visits, yuan, and other standard units.
Constraints for model access and configuration
Monthly updated bulk data requires the model context window to support mixed loading of multiple documents. This prevents context overflow during single processing. Mixed input of multiple document types requires clear processing rules for different documents during configuration. This avoids field confusion and information misalignment. Sensitive data related to safety rectification and subsidy audit needs dedicated access tokens to isolate permissions. This complies with regulatory data security requirements. Format differences across multiple data sources require embedded and generative model parameters to adapt to mixed input of structured and unstructured text.
Configuration settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to mixed input of monthly bulk filing, rectification ledger, and subsidy application documents, avoiding context overflow |
maxTokens | 2000–3000 token | Adapts to response generation for single subsidy application or rectification report, meeting requirements for detailed explanations |
toolChoice | auto | Automatically calls tools to verify structured data, suitable for scenarios such as filing information and bed data verification |
temperature | 0.2–0.4 | Maintains rigor of responses, avoiding subjective deviations in regulatory-related content |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Connects to local or cloud embedded and large model services, adapting to API calls across multiple data sources |
AIPROXY_API_TOKEN | Fill in according to the dedicated access token assigned by permissions | Isolates access permissions across different departments, complying with regulatory data security requirements |
Three common configuration errors
- Embedded model calls fail, returning connection timeout or 401 authentication errors. The cause is incorrect configuration of
AIPROXY_API_ENDPOINTto point to the target model service address, orAIPROXY_API_TOKENnot granting call permissions for the corresponding model. - Model-generated responses are unexpectedly truncated, losing key regulatory information. The cause is failure to adjust the
maxTokensparameter, or setting an excessively small value that cannot accommodate complete rectification report or subsidy application description content. - Reranking model returns empty content. The cause is failure to adapt to the input format of regulatory structured data, or
quoteMaxTokenset too low, resulting in truncated retrieved key content that cannot meet the input requirements of the reranking model.
How to confirm successful configuration
- Call the test interface with a single institutional filing material, check whether the model can correctly extract institutional name and bed count fields, and verify the configuration effects of
toolChoiceandtemperature. - View API call logs, confirm that the request status codes for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare 200, with no authentication or connection errors. - Input monthly bulk rectification ledger data, check that the generated response length matches the settings of
maxTokensandmaxContext, with no context overflow error messages. - Simulate cross-department collaborative query requests, confirm that tokens with different permissions can normally access corresponding data ranges, and verify the effectiveness of security configurations.
The values provided are common starting points and should be evaluated against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.